import os
import time
import uuid
from typing import List, Tuple, Optional, Dict, Union
import google.generativeai as genai
import gradio as gr
from PIL import Image
from openai import OpenAI
print("google-generativeai:", genai.__version__)
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
TITLE = """Gemini PolyGLOT that can understand languages beyond english
"""
SUBTITLE = """"""
DUPLICATE = """
"""
AVATAR_IMAGES = (
None,
"https://media.roboflow.com/spaces/gemini-icon.png"
)
IMAGE_CACHE_DIRECTORY = "/tmp"
IMAGE_WIDTH = 512
CHAT_HISTORY = List[Tuple[Optional[Union[Tuple[str], str]], Optional[str]]]
def translate_to_english(text):
response = client.chat.completions.create(model = "gpt-3.5-turbo",
messages = [
{"role": "system", "content": "You are a translator to English. Check if the input is in English. If the input is english DO NOTHING and return AS-IS else Translate to English"},
{"role": "user", "content": text},
],stream = False, )
return response.choices[0].message.content
def translate_from_english(text, language):
response = client.chat.completions.create(model = "gpt-3.5-turbo",
messages = [
{"role": "system", "content": "You are a translator to " + language + " Translate english to " + language},
{"role": "user", "content": text},
],stream = False, )
return response.choices[0].message.content
def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]:
if not stop_sequences:
return None
return [sequence.strip() for sequence in stop_sequences.split(",")]
def preprocess_image(image: Image.Image) -> Optional[Image.Image]:
image_height = int(image.height * IMAGE_WIDTH / image.width)
return image.resize((IMAGE_WIDTH, image_height))
def cache_pil_image(image: Image.Image) -> str:
image_filename = f"{uuid.uuid4()}.jpeg"
os.makedirs(IMAGE_CACHE_DIRECTORY, exist_ok=True)
image_path = os.path.join(IMAGE_CACHE_DIRECTORY, image_filename)
image.save(image_path, "JPEG")
return image_path
def preprocess_chat_history(
history: CHAT_HISTORY
) -> List[Dict[str, Union[str, List[str]]]]:
messages = []
for user_message, model_message in history:
if isinstance(user_message, tuple):
pass
elif user_message is not None:
messages.append({'role': 'user', 'parts': [user_message]})
if model_message is not None:
messages.append({'role': 'model', 'parts': [model_message]})
return messages
def upload(files: Optional[List[str]], chatbot: CHAT_HISTORY) -> CHAT_HISTORY:
for file in files:
image = Image.open(file).convert('RGB')
image = preprocess_image(image)
image_path = cache_pil_image(image)
chatbot.append(((image_path,), None))
return chatbot
def user(text_prompt: str, chatbot: CHAT_HISTORY):
if text_prompt:
chatbot.append((text_prompt, None))
return "", chatbot
def bot(
google_key: str,
files: Optional[List[str]],
temperature: float,
max_output_tokens: int,
stop_sequences: str,
top_k: int,
top_p: float,
chatbot: CHAT_HISTORY,
language: str
):
if len(chatbot) == 0:
return chatbot
google_key = google_key if google_key else GOOGLE_API_KEY
if not google_key:
raise ValueError(
"GOOGLE_API_KEY is not set. "
"Please follow the instructions in the README to set it up.")
genai.configure(api_key=google_key)
generation_config = genai.types.GenerationConfig(
temperature=temperature,
max_output_tokens=max_output_tokens,
stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences),
top_k=top_k,
top_p=top_p)
if files:
text_prompt = [chatbot[-1][0]] \
if chatbot[-1][0] and isinstance(chatbot[-1][0], str) \
else []
image_prompt = [Image.open(file).convert('RGB') for file in files]
model = genai.GenerativeModel('gemini-pro-vision')
response = model.generate_content(
text_prompt + image_prompt,
stream=True,
generation_config=generation_config)
else:
text_prompt = [chatbot[-1][0]] \
if chatbot[-1][0] and isinstance(chatbot[-1][0], str) \
else []
if (language.startswith("en") == False):
text_prompt = [translate_to_english(text_prompt[0])]
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(
text_prompt,
stream=True,
generation_config=generation_config)
# streaming effect
chatbot[-1][1] = ""
for chunk in response:
for i in range(0, len(chunk.text), 10):
section = chunk.text[i:i + 10]
chatbot[-1][1] += section
time.sleep(0.01)
yield chatbot
google_key_component = gr.Textbox(
label="GOOGLE API KEY",
value="",
type="password",
placeholder="...",
info="Yrly",
visible=GOOGLE_API_KEY is None
)
chatbot_component = gr.Chatbot(
label='Gemini',
bubble_full_width=False,
avatar_images=AVATAR_IMAGES,
scale=2,
height=400
)
text_prompt_component = gr.Textbox(
placeholder="Hi there! [press Enter]", show_label=False, autofocus=True, scale=8
)
upload_button_component = gr.UploadButton(
label="Upload Images", file_count="multiple", file_types=["image"], scale=1
)
run_button_component = gr.Button(value="Run", variant="primary", scale=1)
temperature_component = gr.Slider(
minimum=0,
maximum=1.0,
value=0.4,
step=0.05,
label="Temperature",
info=(
"Temperature controls the degree of randomness in token selection. Lower "
"temperatures are good for prompts that expect a true or correct response, "
"while higher temperatures can lead to more diverse or unexpected results. "
))
max_output_tokens_component = gr.Slider(
minimum=1,
maximum=2048,
value=1024,
step=1,
label="Token limit",
info=(
"Token limit determines the maximum amount of text output from one prompt. A "
"token is approximately four characters. The default value is 2048."
))
stop_sequences_component = gr.Textbox(
label="Add stop sequence",
value="",
type="text",
placeholder="STOP, END",
info=(
"A stop sequence is a series of characters (including spaces) that stops "
"response generation if the model encounters it. The sequence is not included "
"as part of the response. You can add up to five stop sequences."
))
top_k_component = gr.Slider(
minimum=1,
maximum=40,
value=32,
step=1,
label="Top-K",
info=(
"Top-k changes how the model selects tokens for output. A top-k of 1 means the "
"selected token is the most probable among all tokens in the model’s "
"vocabulary (also called greedy decoding), while a top-k of 3 means that the "
"next token is selected from among the 3 most probable tokens (using "
"temperature)."
))
top_p_component = gr.Slider(
minimum=0,
maximum=1,
value=1,
step=0.01,
label="Top-P",
info=(
"Top-p changes how the model selects tokens for output. Tokens are selected "
"from most probable to least until the sum of their probabilities equals the "
"top-p value. For example, if tokens A, B, and C have a probability of .3, .2, "
"and .1 and the top-p value is .5, then the model will select either A or B as "
"the next token (using temperature). "
))
user_inputs = [
text_prompt_component,
chatbot_component
]
language = gr.Textbox(label="description", value = "en")
bot_inputs = [
google_key_component,
upload_button_component,
temperature_component,
max_output_tokens_component,
stop_sequences_component,
top_k_component,
top_p_component,
chatbot_component,
language
]
with gr.Blocks() as demo:
gr.HTML(TITLE)
gr.HTML(SUBTITLE)
with gr.Column():
google_key_component.render()
chatbot_component.render()
with gr.Row():
text_prompt_component.render()
upload_button_component.render()
run_button_component.render()
with gr.Accordion("Parameters", open=False):
temperature_component.render()
max_output_tokens_component.render()
stop_sequences_component.render()
with gr.Accordion("Advanced", open=False):
top_k_component.render()
top_p_component.render()
run_button_component.click(
fn=user,
inputs=user_inputs,
outputs=[text_prompt_component, chatbot_component],
queue=False
).then(
fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
)
text_prompt_component.submit(
fn=user,
inputs=user_inputs,
outputs=[text_prompt_component, chatbot_component],
queue=False
).then(
fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
)
upload_button_component.upload(
fn=upload,
inputs=[upload_button_component, chatbot_component],
outputs=[chatbot_component],
queue=False
)
demo.queue(max_size=99).launch(debug=False, show_error=True)